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Identification and Classification of Buckwheat Grain by Microfocus Radiography and Hyperspectral Imaging Methods.

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Title: Identification and Classification of Buckwheat Grain by Microfocus Radiography and Hyperspectral Imaging Methods.
Authors: Platov, Yu. T.1 (AUTHOR) Platov.YT@rea.ru, Beletskii, S. L.2 (AUTHOR) grain-miller@yandex.ru, Metlenkin, D. A.1 (AUTHOR) dametl@mail.ru, Platova, R. A.1 (AUTHOR) Platova.RA@rea.ru, Vereshchagin, A. L.3 (AUTHOR) val@bti.secna.ru, Marin, V. A.3 (AUTHOR) tehbiysk@mail.ru
Source: Russian Journal of Nondestructive Testing. Apr2024, Vol. 60 Issue 4, p446-454. 9p.
Subjects: Principal components analysis, Buckwheat, Image analysis, X-ray imaging, Multivariate analysis
Abstract: Classification of buckwheat grains is important because the absence of defective grains is a guarantee of yield and quality. Buckwheat grains were randomly selected from a batch with grains that varied in quality. The identification and classification of buckwheat grains according to the degree of fulfillment was carried out by a combination of microfocus X-ray and hyperspectral image analysis and multivariate analysis techniques. Using microfocus radiography, buckwheat grains were categorized into groups according to the degree of fulfillment. Hyperspectral image of buckwheat grains in the range of 935–1720 nm was acquired using a Specim FX17 camera. Using the polygon selection function, the averaged spectra were obtained and a data matrix of grain samples was generated. The bands of the spectrum contributing most to the grading of the grain samples by the degree of fulfillment were identified using the principal component analysis. The classification model of grading buckwheat grain into groups by the degree of fulfillment was constructed by partial least squares discriminant analysis method. The results showed that hyperspectral image is a potential tool for rapid and accurate identification of buckwheat grains, which can be used in large-scale grain classification and grain quality determination. [ABSTRACT FROM AUTHOR]
Copyright of Russian Journal of Nondestructive Testing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Identification and Classification of Buckwheat Grain by Microfocus Radiography and Hyperspectral Imaging Methods.
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  Data: <searchLink fieldCode="JN" term="%22Russian+Journal+of+Nondestructive+Testing%22">Russian Journal of Nondestructive Testing</searchLink>. Apr2024, Vol. 60 Issue 4, p446-454. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Buckwheat%22">Buckwheat</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22X-ray+imaging%22">X-ray imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink>
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  Data: Classification of buckwheat grains is important because the absence of defective grains is a guarantee of yield and quality. Buckwheat grains were randomly selected from a batch with grains that varied in quality. The identification and classification of buckwheat grains according to the degree of fulfillment was carried out by a combination of microfocus X-ray and hyperspectral image analysis and multivariate analysis techniques. Using microfocus radiography, buckwheat grains were categorized into groups according to the degree of fulfillment. Hyperspectral image of buckwheat grains in the range of 935–1720 nm was acquired using a Specim FX17 camera. Using the polygon selection function, the averaged spectra were obtained and a data matrix of grain samples was generated. The bands of the spectrum contributing most to the grading of the grain samples by the degree of fulfillment were identified using the principal component analysis. The classification model of grading buckwheat grain into groups by the degree of fulfillment was constructed by partial least squares discriminant analysis method. The results showed that hyperspectral image is a potential tool for rapid and accurate identification of buckwheat grains, which can be used in large-scale grain classification and grain quality determination. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Russian Journal of Nondestructive Testing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Apr2024
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